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coderabbit-data-handlingCoderabbit 数据处理

Agent Skill

用于辅助数据整理、表格处理、CSV/Excel 分析、指标计算和图表准备。它适合让 Agent 清洗字段、汇总数据、发现异常、生成统计口径或把分析结果转成可读说明。使用时需要确认数据来源、字段含义和时间范围,避免把样本数据当全量事实;涉及敏感数据、导出文件或批量写回时,应先确认权限和脱敏边界。

总安装

612

周安装

25

GitHub Stars

2,104

下载量

198
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:coderabbit-data-handling(Coderabbit 数据处理)
来源仓库:https://github.com/jeremylongshore/claude-code-plugins-plus-skills
仓库路径:skills/coderabbit-data-handling
安装命令:
npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill coderabbit-data-handling
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill coderabbit-data-handling

简介

coderabbit-data-handling 管理代码审查中的敏感数据与 secrets 扫描策略。

  • 适用于排除 .env、密钥文件等敏感内容进入 AI 分析范围。
  • 支持 path_filters 配置与 review comment 数据保留周期设定。
  • 安装前应确认是否允许读取仓库配置与 secrets scanning 工具集成。
  • 建议结合企业安全策略定制过滤规则,避免泄露内部凭证信息。

SKILL.md

CodeRabbit Data Handling

Overview

Manage code review data and sensitive patterns with CodeRabbit. Covers secret detection in PRs, sensitive file exclusion from AI review, review comment data retention, and configuring what code context gets sent to the AI engine.

Prerequisites

  • CodeRabbit installed on repository
  • Understanding of sensitive file patterns
  • Repository admin access for configuration
  • Secret scanning tools awareness

Instructions

Step 1: Exclude Sensitive Files from Review

# .coderabbit.yaml - Data handling configuration
reviews:
  path_filters:
    # Never send these to AI review
    - "!**/.env*"
    - "!**/credentials*"
    - "!**/secrets*"
    - "!**/*.pem"
    - "!**/*.key"
    - "!**/*.p12"
    - "!**/serviceAccountKey*"
    - "!**/terraform.tfstate*"
    - "!**/*.tfvars"

    # Exclude large generated files
    - "!**/package-lock.json"
    - "!**/pnpm-lock.yaml"
    - "!**/yarn.lock"
    - "!**/*.generated.*"
    - "!**/dist/**"
    - "!**/coverage/**"

Step 2: Secret Detection Instructions

# .coderabbit.yaml - Instruct AI to flag secrets
reviews:
  path_instructions:
    - path: "**"
      instructions: |
        CRITICAL: Flag any of these patterns as HIGH SEVERITY:
        - Hardcoded API keys, tokens, or passwords
        - AWS access keys (AKIA...)
        - Private keys or certificates
        - Database connection strings with credentials
        - JWT secrets or signing keys
        - Webhook URLs with tokens in query params

        If you find any secrets, add a comment:
        "SECURITY: Hardcoded secret detected. Move to environment variable."

    - path: "**/*.{yml,yaml}"
      instructions: |
        Check CI/CD files for:
        - Secrets logged in step names or echo statements
        - Unpinned GitHub Actions (use SHA, not tags)
        - Missing secret masking in outputs

Step 3: Review Data Scope Management

# Control what context CodeRabbit accesses
reviews:
  auto_review:
    enabled: true
    drafts: false   # Don't review draft PRs (may contain WIP secrets)
    base_branches:
      - "main"
      - "develop"
    ignore_title_keywords:
      - "WIP"
      - "DO NOT REVIEW"
      - "DRAFT"

  # Limit file types reviewed
  path_filters:
    # Only review source code, not data
    - "+src/**"
    - "+lib/**"
    - "+app/**"
    - "+tests/**"
    - "+.github/**"
    - "!**/*.csv"
    - "!**/*.json"      # Exclude data files
    - "!**/fixtures/**"  # Exclude test fixtures with sample data
    - "!**/seeds/**"     # Exclude database seeds

Step 4: Sensitive Code Pattern Detection

# .coderabbit.yaml - Custom pattern detection
reviews:
  path_instructions:
    - path: "src/db/**"
      instructions: |
        Review database code for:
        - SQL injection vulnerabilities (string concatenation in queries)
        - Unparameterized queries
        - PII logged in error messages
        - Missing data sanitization on inputs

    - path: "src/api/**"
      instructions: |
        Review API endpoints for:
        - User input not validated before processing
        - Sensitive data in response bodies (passwords, tokens)
        - Missing authentication checks
        - Overly permissive CORS configuration
        - PII in URL parameters (should be POST body instead)

    - path: "src/auth/**"
      instructions: |
        SECURITY-CRITICAL PATH. Review for:
        - Token expiry configuration
        - Password hashing (must use bcrypt/argon2, never MD5/SHA)
        - Session fixation vulnerabilities
        - CSRF protection

Error Handling

IssueCauseSolution
Secret in reviewed PRNot in exclusion listAdd pattern to path_filters
Large diff reviewedGenerated code includedExclude generated file paths
Sensitive fixture dataTest data has real PIIExclude fixtures directory
Review on draft PRdrafts setting enabledSet drafts: false

Examples

Minimal Secure Configuration

# .coderabbit.yaml - Security-focused setup
reviews:
  auto_review:
    enabled: true
    drafts: false
  path_filters:
    - "!**/.env*"
    - "!**/*.key"
    - "!**/*.pem"
    - "!**/secrets/**"
  path_instructions:
    - path: "**"
      instructions: "Flag any hardcoded secrets, API keys, or credentials."

Output

  • Sensitive files excluded from AI review via path_filters
  • Secret detection instructions configured for all code paths
  • Review scope limited to source code only (not data files)
  • Security-focused path_instructions for database, API, and auth code

Resources

Next Steps

For security hardening, see coderabbit-security-basics.

适合场景

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02

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03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

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能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

38.58%
按下载量换算76

Claude

30.95%
按下载量换算61

Cursor

17.2%
按下载量换算34

Gemini CLI

9.3%
按下载量换算18

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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